Earticle

초록

영어

Fire is an extremely catastrophic disaster that leads to the destruction of forests, human assets, reduced soil fertility, land resources, and the cause of global warming. In the current decade, fire detection and its management are the major concern of several researchers to prevent social, ecological, and economic damages. To overcome such kind of losses, early fire detection, and the automatic response is very significant. Moreover, achieving high accuracy with reducing inference time and model size is also challenging for the Unmanned Aerial Vehicle (UAVs). Therefore, in this work, we enabled the VGG16 architecture for UAV in terms of reducing its learning parameters from 138 million to 11.4 million for early fire detection. The proposed system is inexpensive in terms of computation and size. The performance of our proposed work is evaluated over the custom dataset. We performed comprehensive experiments using various deep learning architectures such as VGG16, ResNet50, and the proposed CNN model. The experimental results based on the proposed model achieved an accuracy of 98% on 50 epochs.

목차

Abstract
I. INTRODUCTION
II. PROPOSED SYSTEM
III. EXPERIMENTAL SETUP AND RESULTS
A. Dataset Details
B. Results
C. Visualized Results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Hikmat Yar [ Sejong University ]
• Samee Ullah Khan [ Sejong University ]
• Noman Khan [ Sejong University ]
• Min Je Kim [ Sejong University ]
• Mi Young Lee [ Sejong University ]
• Sung Wook Baik [ Sejong University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004